Broadening the audience for science engagement with machine-learning techniques
Bibliographic Data
| ID | 22093345 |
|---|---|
| Authors | Fabienne Crettaz Von Roten (0000-0003-1186-5854, University of Lausanne, corresponding author) |
| Year | 2024 |
| Volume | 9 |
| Publication date | 2024-05-21 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Communication (JOURNAL) |
| Journal identifiers | ISSN: 2297-900X • E-ISSN: 2297-900X |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fcomm.2024.1382952 |
| OpenAlex | W4398174931 |
| Language | EN |
| References cited | 12 |
In Switzerland, the need to engage the public in science is rooted in the political system with its tools of initiatives, therefore the broadening of the audience is critically important. Using the 2021 Science and Technology Eurobarometer, we propose solutions by using machine-learning techniques which identified patterns of engagement and the interaction of sociodemographic characteristics that constitute the prediction (1) of lowest level of science engagement and (2) of non-visitors to the science and technology museum. The techniques allow a more precise targeting than traditional segmenttion analyses
Data science · Political science · Public engagement · Public relations · Chemistry · Climate Change Communication and Perception · Computer Science · Misinformation and Its Impacts · Public Relations and Crisis Communication
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| Highly cited | No |
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